The vast amount of available language data calls for appropriate tools to manage it, such as summarization, a well-established field in NLP. However, summarization research has mostly resulted in summaries composed of sentences extracted whole from the text. First, we worked on generating summaries composed of subsentential fragments that are recombined to create new sentences (Xie et al., 2004; Xie et al., 2008). In more recent work, we used information extraction and generation techniques to produce summaries of reviews of songs for a Music Recommendation System (Tata & Di Eugenio, 2010; Tata & Di Eugenio, 2012). One current project applies summarization to the generation of patient-centric summaries of hospital stays, which integrate information that comes from both discharge notes, written by doctors, and nursing notes. Our first result computationally confirms our intuition that there is very scant overlap between the notes written by doctors and by nurses for the same patient (Di Eugenio et al., 2013a).